Mixture of Experts Infrastructure: Scaling Sparse Models for Production AI

Updated December 8, 2025

December 2025 Update: MoE now powers over 60% of open-source AI model releases in 2025. Top 10 models on Artificial Analysis leaderboard (DeepSeek-R1, Kimi K2, Mistral Large 3) all use MoE. NVIDIA GB200 NVL72 delivers 10x performance leap for MoE versus H200. Wide Expert Parallelism achieving 1.8x per-GPU throughput gains. vLLM and TensorRT-LLM adding native MoE optimizations. DeepSeek-V3 reaching 250 TFLOPs/sec/GPU on 256 H100s. Memory remains primary constraint—full DeepSeek-R1 requires 13,719 GB/s bandwidth.

DeepSeek-V3 demonstrates what Mixture of Experts architecture enables: a model with 671 billion total parameters that activates only 37 billion during inference, achieving GPT-4 level performance at a fraction of the compute cost.¹ The architecture that once seemed like academic curiosity now powers nearly every frontier model on public leaderboards. Mixtral, DBRX, Grok, and the entire DeepSeek family prove that sparse activation unlocks scale impossible with dense architectures. Yet deploying MoE models in production introduces infrastructure challenges that traditional LLM serving cannot address. Memory requirements, expert parallelism, routing overhead, and load balancing demand purpose-built infrastructure that few organizations understand how to build.

How Mixture of Experts actually works

MoE architectures divide neural networks into specialized sub-networks called "experts," each focusing on specific input patterns or tasks. Rather than processing every token through the entire model, a gating network (router) dynamically selects which experts handle each input. Mistral's Mixtral 8x7B exemplifies the approach: each transformer layer contains eight expert feedforward networks with 7 billion parameters each, but the router selects only two experts per token.²

The efficiency gains compound dramatically. Mixtral 8x7B contains 46 billion total parameters, but any given forward pass activates only 12 billion parameters—the equivalent computational cost of a much smaller dense model.³ Organizations achieve larger model capacity without proportionally larger inference costs. Training benefits similarly, as gradient updates flow only through activated experts rather than the entire parameter space.

The router network makes all activation decisions. For each token at each layer, the router computes affinity scores across all experts, then selects the top-k experts (typically two) for processing. Router training requires careful auxiliary losses to prevent "expert collapse"—the tendency for routers to funnel all traffic through a small subset of experts while others remain undertrained.⁴

Modern MoE architectures have grown substantially beyond early designs. DBRX uses 16 experts per layer with 4 active during inference, totaling 132 billion parameters.⁵ DeepSeek-V3 scales to 256 experts with 8 active, reaching 671 billion total parameters.⁶ Each architectural choice trades off between model capacity, inference efficiency, and deployment complexity.

Memory: the dominant infrastructure constraint

MoE models present a paradox: they use fewer FLOPs per token than dense models of equivalent quality, but require far more memory. Every expert must reside in accessible memory to enable dynamic routing decisions, even though most experts remain idle for any given token.

DeepSeek-R1 at full activation requires 13,719 GB/s memory bandwidth—achievable only with data center systems like NVIDIA DGX-H100 consuming over 10,000 watts.⁷ The requirement stems from loading expert weights on-demand as the router makes activation decisions. At batch size 1 (typical for single-user inference), bandwidth requirements drop to 1,040 GB/s, manageable with consumer accelerators like RTX 4090, but production serving at scale cannot rely on such constrained conditions.

Mixtral-8x22B and DeepSeek-V3 consume hundreds of gigabytes, significantly exceeding dense models with similar effective compute.⁸ A single 80GB GPU cannot hold these models, forcing multi-GPU deployments even for basic functionality. The memory footprint scales with total parameters, not activated parameters, undermining the computational efficiency that makes MoE attractive.

Memory pressure intensifies in production environments. Beyond model weights, systems must maintain KV caches for active sequences. MoE models with many layers multiply KV cache requirements. Batching multiple requests—essential for throughput optimization—further compounds memory consumption. Production deployments routinely require 8+ GPUs with 80GB each simply to load modern MoE models before serving any traffic.

CPU offloading provides partial relief for memory-constrained environments. Approaches like Pre-gated MoE predict expert activations ahead of inference, allowing prefetching of required expert weights from CPU memory to GPU before tokens arrive.⁹ The technique enables single-GPU deployment of large MoE models, though latency increases from the CPU-GPU transfer overhead.

Expert parallelism distributes the load

Expert parallelism (EP) addresses memory constraints by distributing experts across multiple GPUs rather than replicating the entire model on each device.¹⁰ Different experts reside on different GPUs, with explicit communication routing tokens to their assigned experts during forward passes.

The parallelism strategy differs fundamentally from tensor parallelism used for dense models. Tensor parallelism splits individual matrix operations across GPUs, requiring communication at every layer. Expert parallelism keeps each expert's operations local to a single GPU, communicating only when routing decisions direct tokens elsewhere. The communication pattern maps well to MoE's sparse activation: only tokens destined for remote experts require network transfers.

Implementing effective expert parallelism requires sophisticated distributed systems engineering. Microsoft DeepSpeed-MoE combines expert parallelism with data parallelism and tensor slicing, coordinating communication schedules across parallelism dimensions.¹¹ The framework routes tokens to appropriate GPUs, aggregates results, and maintains training gradients across the distributed system.

Large-scale expert parallelism (distributing across 8+ GPUs) introduces additional optimization opportunities. With fewer experts per GPU, each device faces less weight-loading pressure and achieves higher arithmetic intensity in its GEMM operations.¹² NVIDIA's Wide Expert Parallelism (Wide-EP) on GB200 NVL72 achieves up to 1.8x higher per-GPU throughput compared to smaller EP configurations by leveraging this effect.

The GPU interconnect topology critically impacts expert parallelism performance. High-bandwidth fabrics like NVLink and NVSwitch enable efficient token routing between GPUs within a node. InfiniBand extends this across nodes for cluster-scale deployments. Latency-sensitive communication patterns make network architecture as important as raw compute capability.

Inference serving demands specialized optimization

Production MoE serving requires inference frameworks with native support for sparse architectures. Both vLLM and TensorRT-LLM added MoE-specific optimizations throughout 2025, recognizing that general-purpose LLM serving cannot efficiently handle expert routing patterns.

vLLM's January 2025 V1 release includes FlashInfer integration with autotuning for MoE kernels.¹³ Because optimal kernel implementations depend heavily on batch sizes and sequence lengths, vLLM benchmarks various tactics during startup to select the best performing configuration. The PagedAttention foundation that makes vLLM efficient for dense models transfers directly to MoE serving, managing KV cache memory across the larger parameter footprints.

TensorRT-LLM provides NVIDIA-optimized MoE inference with custom attention kernels, inflight batching, and quantization down to FP4 and INT4.¹⁴ On H100 with FP8, the framework reaches over 10,000 output tokens per second at peak throughput for 64 concurrent requests. Qwen3 MoE models achieve up to 16x inference throughput speedups compared to BF16 baselines when deployed with TensorRT-LLM optimizations.

The llm-d project launched in May 2025 by Red Hat, Google Cloud, IBM Research, NVIDIA, and CoreWeave provides Kubernetes-native distributed serving built atop vLLM.¹⁵ The orchestration layer handles the complexity of distributing MoE workloads across cluster resources, managing expert placement, and scaling serving capacity dynamically.

Practical production deployments achieve 300-600 tokens per second per user at under 420GB VRAM for 1.8 trillion parameter MoE models when properly optimized.¹⁶ Reaching these performance levels requires compilation with vLLM or TensorRT-LLM, continuous batching, paged KV cache, and FlashAttention-3. Organizations lacking inference optimization expertise should expect significant performance gaps from naive deployments.

Training infrastructure scales to thousands of GPUs

Training MoE models at scale historically required specialized infrastructure and deep distributed systems expertise. Recent framework improvements democratize access, though frontier-scale training still demands substantial resources.

Databricks collaborated with the PyTorch team to scale MoE training to over 3,000 GPUs using PyTorch Distributed and MegaBlocks.¹⁷ The work demonstrates that standard distributed training frameworks can handle MoE's unique communication patterns when properly configured. DeepSeek-V3 achieved 250 TFLOPs per second per GPU on 256 H100s—performance that approaches theoretical hardware limits.¹⁸

Eight GPUs with 80GB memory each represent the minimum configuration for reproducing benchmark results and running fine-tuning experiments efficiently.¹⁹ Production training runs typically use far more. The GPU count trades off against training time: more GPUs enable larger batch sizes and faster iteration, while smaller configurations extend training duration proportionally.

Load balancing presents the primary training challenge beyond raw compute. Router networks can converge to states routing most tokens to a few "popular" experts while leaving others undertrained.²⁰ Auxiliary loss functions encourage uniform expert utilization, but tuning these losses requires experimentation. Poorly balanced experts degrade model quality and waste capacity on undertrained parameters that never contribute meaningfully to outputs.

Memory fragmentation compounds load balancing challenges during training. Dynamic expert activation creates irregular memory access patterns that fragment GPU memory over time. Garbage collection pauses and allocation failures can interrupt long training runs. Production training infrastructure must account for these patterns through careful memory management and monitoring.

Hardware evolution accelerates MoE capability

NVIDIA's Blackwell architecture brings specific optimizations for MoE workloads. The GB200 NVL72 rack-scale system delivers 10x performance improvements for MoE models compared to HGX H200.²¹ The Kimi K2 Thinking model demonstrates these gains in practice, with benchmark results validating the architectural improvements translate to real workload acceleration.

The performance leap stems from multiple factors: increased memory bandwidth, higher NVLink throughput for expert parallelism communication, and compute improvements that accelerate the small-batch GEMM operations characteristic of sparse expert activation. MoE's unique computation patterns benefit disproportionately from these specific improvements.

Cloud providers rapidly deployed GB200 NVL72 systems throughout 2025. AWS, Azure, Google Cloud, CoreWeave, Lambda, Oracle, and others offer access to the architecture specifically because MoE workloads drive demand.²² Organizations training or serving frontier MoE models increasingly require Blackwell-class hardware to achieve competitive performance.

The hardware trajectory suggests MoE's infrastructure advantages will compound over successive generations. Architecture designed for sparse, dynamic workloads will continue improving faster than general-purpose accelerators optimizing for dense computation. Organizations investing in MoE infrastructure today position themselves for accelerating returns as hardware evolves.

Deployment patterns for production MoE systems

Successful MoE deployments follow patterns that address the architecture's unique requirements.

Right-size GPU count for memory, not compute. MoE models require GPU memory for total parameters, not activated parameters. Size deployments based on model weight storage plus KV cache requirements at target batch sizes, then verify compute throughput meets latency objectives. Organizations frequently under-provision memory by reasoning about activated parameter counts.

Implement expert placement strategies. Distributing experts across GPUs affects routing latency and load balance. Place frequently co-activated experts on the same GPU to minimize cross-device communication. Profile routing patterns on representative workloads before finalizing placement decisions.

Monitor expert utilization continuously. Router drift can shift traffic patterns over time, creating hot-spot experts that bottleneck throughput while cold experts sit idle. Real-time monitoring identifies utilization imbalances before they significantly impact performance. Consider load-balancing mechanisms that supplement router decisions during serving.

Plan for memory headroom. MoE serving memory consumption varies more than dense model serving due to dynamic expert activation patterns. Reserve 20-30% memory headroom beyond calculated requirements to handle traffic spikes without out-of-memory failures. The buffer protects against the unpredictable memory patterns characteristic of sparse architectures.

Choose inference frameworks with native MoE support. Generic LLM serving frameworks impose overhead that MoE-optimized alternatives avoid. vLLM and TensorRT-LLM both provide MoE-specific optimizations that deliver measurable throughput improvements. Evaluate both frameworks on target workloads before committing to production deployments.

Introl's infrastructure deployment expertise extends to MoE-specific architectures across our global coverage area. Whether deploying GB200 NVL72 clusters for frontier model training or optimizing inference serving for Mixtral-class models, infrastructure decisions benefit from experience with these unique deployment patterns.

The MoE infrastructure imperative

Mixture of Experts has transitioned from architectural experiment to production necessity. Over 60% of 2025's open-source model releases use MoE architectures, and every model atop the Artificial Analysis intelligence leaderboard employs sparse expert activation.²³ Organizations building AI infrastructure must develop MoE-specific capabilities or accept competitive disadvantage against those who do.

The infrastructure requirements differ substantially from dense model deployments. Memory dominates over compute as the primary constraint. Expert parallelism introduces communication patterns absent from tensor-parallel dense serving. Training requires sophisticated load balancing to prevent expert collapse. Inference demands frameworks with native MoE optimization to achieve competitive throughput.

Hardware evolution favors MoE economics. Each generation of accelerators improves memory bandwidth and interconnect speeds faster than raw compute, directly benefiting the sparse computation patterns that define MoE architectures. GB200 NVL72's 10x performance improvement for MoE workloads demonstrates the trajectory.

Organizations planning AI infrastructure investments should prioritize MoE capability. The architecture powers today's most capable models and will likely dominate the next generation. Infrastructure designed around dense model assumptions will require expensive retrofitting as MoE becomes the default. Building MoE-native infrastructure now avoids the technical debt that legacy architectures accumulate.

The sparse computation paradigm represents a fundamental shift in how AI systems achieve scale. Dense models hit practical limits where adding parameters yields diminishing returns relative to compute costs. MoE circumvents these limits by activating only the capacity each input requires. The infrastructure that enables this efficiency will define which organizations can deploy frontier AI capabilities—and which cannot.

References

1. DeepSeek. "DeepSeek-V3 Technical Report." DeepSeek, December 2024. https://github.com/deepseek-ai/DeepSeek-V3

2. Hugging Face. "Mixture of Experts Explained." Hugging Face Blog, 2024. https://huggingface.co/blog/moe

3. ———. "Mixture of Experts Explained." Hugging Face Blog, 2024.

4. IBM. "What is mixture of experts?" IBM Think, 2025. https://www.ibm.com/think/topics/mixture-of-experts

5. AIML API. "DBRX, Grok, Mixtral: Mixture-of-Experts is a trending architecture for LLMs." AIML API Blog, 2024. https://aimlapi.com/blog/dbrx-grok-mixtral-mixture-of-experts-is-a-trending-architecture-for-llms

6. Gocodeo. "Mixture of Experts (MoE) Explained: The Architecture Powering Models." Gocodeo, 2025. https://www.gocodeo.com/post/mixture-of-experts-moe-explained-the-architecture-powering-models

7. MoE-Lens. "MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints." arXiv, 2025. https://arxiv.org/html/2504.09345v1

8. ———. "MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints." arXiv, 2025.

9. Pre-gated MoE. "Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference." ISCA-51, 2024. https://arxiv.org/html/2308.12066v3

10. NVIDIA. "Scaling Large MoE Models with Wide Expert Parallelism on NVL72 Rack Scale Systems." NVIDIA Technical Blog, 2025. https://developer.nvidia.com/blog/scaling-large-moe-models-with-wide-expert-parallelism-on-nvl72-rack-scale-systems/

11. DeepSpeed. "DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training." ICML, 2022. https://proceedings.mlr.press/v162/rajbhandari22a/rajbhandari22a.pdf

12. NVIDIA. "Scaling Large MoE Models with Wide Expert Parallelism on NVL72 Rack Scale Systems." NVIDIA Technical Blog, 2025.

13. vLLM Blog. "SemiAnalysis InferenceMAX: vLLM and NVIDIA Accelerate Blackwell Inference." vLLM Blog, October 2025. https://blog.vllm.ai/2025/10/09/blackwell-inferencemax.html

14. NVIDIA. "Integrate and Deploy Tongyi Qwen3 Models into Production Applications with NVIDIA." NVIDIA Technical Blog, 2025. https://developer.nvidia.com/blog/integrate-and-deploy-tongyi-qwen3-models-into-production-applications-with-nvidia/

15. Red Hat. "Why vLLM is the best choice for AI inference today." Red Hat Developer, October 2025. https://developers.redhat.com/articles/2025/10/30/why-vllm-best-choice-ai-inference-today

16. Medium. "Optimizing LLM Inference." Medium, November 2025. https://medium.com/@nimritakoul01/optimizing-llm-inference-63f63cecda6a

17. PyTorch. "Training MoEs at Scale with PyTorch." PyTorch Blog, 2025. https://pytorch.org/blog/training-moes/

18. NVIDIA. "Accelerating Large-Scale Mixture-of-Experts Training in PyTorch." NVIDIA Technical Blog, 2025. https://developer.nvidia.com/blog/accelerating-large-scale-mixture-of-experts-training-in-pytorch/

19. Deepchecks. "MoE in LLMs: Cutting Costs & Boost Performance with Experts." Deepchecks, 2025. https://www.deepchecks.com/moe-llms-cost-efficiency-performance-expert-network/

20. Hugging Face. "Mixture of Experts Explained." Hugging Face Blog, 2024.

21. NVIDIA. "Mixture of Experts Powers the Most Intelligent Frontier AI Models, Runs 10x Faster on NVIDIA Blackwell NVL72." NVIDIA Blog, 2025. https://blogs.nvidia.com/blog/mixture-of-experts-frontier-models/

22. ———. "Mixture of Experts Powers the Most Intelligent Frontier AI Models, Runs 10x Faster on NVIDIA Blackwell NVL72." NVIDIA Blog, 2025.

23. ———. "Mixture of Experts Powers the Most Intelligent Frontier AI Models, Runs 10x Faster on NVIDIA Blackwell NVL72." NVIDIA Blog, 2025.

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Over 60% of 2025's AI models use Mixture of Experts. Learn the infrastructure requirements for deploying sparse architectures that power DeepSeek and Mixtral.

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Key takeaways

For infrastructure architects: - MoE powers 60%+ of 2025 open-source AI releases; all top 10 models on Artificial Analysis use MoE - DeepSeek-V3: 671B total params, 37B active per inference—GPT-4 performance at fraction of compute - Memory constraint: full DeepSeek-R1 requires 13,719 GB/s bandwidth; production needs 8+ GPUs with 80GB each

For ML engineers: - Mixtral 8x7B: 46B total params, 12B active per forward pass (2 of 8 experts per token) - DeepSeek-V3 achieved 250 TFLOPs/sec/GPU on 256 H100s—approaches theoretical limits - Wide Expert Parallelism on GB200 NVL72: 1.8x per-GPU throughput vs smaller EP configs

For capacity planning: - GB200 NVL72 delivers 10x MoE performance improvement vs HGX H200 - Minimum training config: 8 GPUs with 80GB each for benchmark reproduction - Reserve 20-30% memory headroom—MoE consumption varies more than dense models

For production deployments: - vLLM V1 (Jan 2025): FlashInfer + autotuning for MoE kernels; TensorRT-LLM achieves 10K+ tokens/sec - 300-600 tokens/sec/user achievable at <420GB VRAM for 1.8T parameter MoE when optimized - CPU offloading (Pre-gated MoE) enables single-GPU deployment with latency tradeoff